Metabolic cost of neuronal information in an empirical stimulus-response model

Biological Cybernetics

Volumn 107, Issue 3, 2013, Pages 355-365

  Kostal, Lubomir ^a   Lansky, Petr ^a   McDonnell, Mark D ^b  

^a INSTITUTE OF PHYSIOLOGY   (Czech Republic)

^b UNIVERSITY OF SOUTH AUSTRALIA   (Australia)

Author keywords

Information capacity; Metabolic cost; Stimulus response curve

Indexed keywords

INFORMATION TRANSFERS; INFORMATION TRANSMISSION; INPUT DISTRIBUTIONS; METABOLIC COST; MUTUAL INFORMATIONS; OPTIMAL INPUT DISTRIBUTION; STATISTICAL PROPERTIES; STIMULUS-RESPONSE CURVE;

CHANNEL CAPACITY; COSTS; INFORMATION THEORY; METABOLISM; OPTIMIZATION;

NEURONS;

ADENOSINE TRIPHOSPHATE;

ACTION POTENTIAL; ANIMAL; ARTICLE; BIOLOGICAL MODEL; BRAIN; CYTOLOGY; HUMAN; INFORMATION SCIENCE; METABOLISM; NERVE CELL; PHYSIOLOGY; SYNAPTIC TRANSMISSION;

ACTION POTENTIALS; ADENOSINE TRIPHOSPHATE; ANIMALS; BRAIN; HUMANS; INFORMATION THEORY; MODELS, NEUROLOGICAL; NEURONS; SYNAPTIC TRANSMISSION;

EID: 84878529420     PISSN: 03401200     EISSN: 14320770     Source Type: Journal    
DOI: 10.1007/s00422-013-0554-6     Document Type: Article

References (74)

1. Abou-Faycal IC, Trott MD, Shamai S (2001) The capacity of discrete-time memoryless Rayleigh-fading channels. IEEE Trans Inf Theory 47(4):1290-1301 (Pubitemid 32508474)
2. Abramowitz M, Stegun IA (1965) Handbook of mathematical functions, with formulas, graphs, and mathematical tables. Dover, New York
3. Alexander RM (1996) Optima for animals. Princeton University Press, Princeton
4. Atick JJ (1992) Could information theory provide an ecological theory of sensory processing? Netw Comput Neural Syst 3(2):213-251
5. Attwell D, Laughlin SB (2001) An energy budget for signaling in the grey matter of the brain. J Cereb Blood Flow Metab 21(10):1133-1145 (Pubitemid 32947479)
6. Baddeley R, Abbott LF, Booth MCA, Sengpiel F, Freeman T, Wakeman EA, Rolls ET (1997) Responses of neurons in primary and inferior temporal visual cortices to natural scenes. Proc Roy Soc B 264:1775-1783 (Pubitemid 28021588)
7. Balasubramanian V, Berry MJ (2002) A test of metabolically efficient coding in the retina. Netw Comput Neural Syst 13:531-552
8. Barlow HB (1961) Possible principles underlying the transformation of sensory messages. In: Rosenblith W (ed) Sensory Communication. MIT Press, Cambridge, pp 217-234
9. Bernardo JM (1979) Reference posterior distributions for Bayesian inference. J Roy Stat Soc B 41:113-147
10. Blahut R (1972) Computation of channel capacity and rate-distortion functions. IEEE Trans Inf Theory 18(4):460-473
11. Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge
12. Brunel N, Nadal JP (1998) Mutual information, Fisher information, and population coding. Neural Comput 10(7):1731-1757 (Pubitemid 128463681)
13. Carandini M (2004) Amplification of trial-to-trial response variability by neurons in visual cortex. PLoS Biol 2(9):e264 (Pubitemid 39245255)
14. Chan TH, Hranilovic S, Kschischang FR (2005) Capacity-achieving probability measure for conditionally Gaussian channels with bounded inputs. IEEE Trans Inf Theory 51:2073-2088 (Pubitemid 40843626)
15. Chiang M, Boyd S (2004) Geometric programming duals of channel capacity and rate distortion. IEEE Trans Inf Theory 50:245-258
16. Clarke BS, Barron AR (1990) Information-theoretic asymptotics of Bayes methods. IEEE Trans Inf Theory 36(3):453-471 (Pubitemid 20713732)
17. Cover TM, Thomas JA (1991) Elements of information theory. Wiley, New York
18. Dauwels J (2005) Numerical computation of the capacity of continuous memoryless channels. In: Cardinal J, Cerf N, Delgrange O (eds) Proceedings of the 26th symposium on information theory in the Benelux. WIC, Brussels, pp 221-228
19. Davis M (1980) Capacity and cutoff rate for Poisson-type channels. IEEE Trans Inf Theory 26(6):710-715 (Pubitemid 11460689)
20. Dimitrov AG, Miller JP (2001) Neural coding and decoding: communication channels and quantization. Netw Comput Neural Syst 12(4):441-472 (Pubitemid 33625157)
21. Fairhall AL, Lewen GD, Bialek W, de Ruyter van Steveninck RR (2001) Efficiency and ambiguity in an adaptive neural code. Nature 412:787-792 (Pubitemid 32801454)
22. Farkhooi F, Müller E, Nawrot MP (2011) Adaptation reduces variability of the neuronal population code. Phys Rev E 83(050):905
23. Gallager RG (1968) Information theory and reliable communication. Wiley, New York
24. Gastpar M, Rimoldi B, Vetterli M (2003) To code, or not to code: Lossy source-channel communication revisited. IEEE Trans Inf Theory 49(5):1147-1158
25. Grant M, Boyd S (2009) Cvx: Matlab software for disciplined convex programming (web page and software). http://stanford.edu/boyd/cvx
26. Greenwood PE, Lansky P (2005) Optimal signal estimation in neuronal models. Neural Comput 17(10):2240-2257 (Pubitemid 41186997)
27. Gremiaux A, Nowotny T, Martinez D, Lucas P, Rospars JP (2012) Modelling the signal delivered by a population of first-order neurons in a moth olfactory system. Brain Res 1434:123-135
28. Huang J, Meyn SP (2005) Characterization and computation of optimal distributions for channel coding. IEEE Trans Inf Theory 51(7):2336-2351 (Pubitemid 41136396)
29. Ikeda S, Manton JH (2009) Capacity of a single spiking neuron channel. Neural Comput 21(6):1714-1748
30. Jacobson H (1950) The informational capacity of the human ear. Science 112(2901):143
31. Johnson DH, Goodman IN (2008) Inferring the capacity of the vector poisson channel with a bernoulli model. Netw Comput Neural Syst 19(1):13-33
32. Johnson N, Kotz S, Balakrishnan N (1994) Continuous univariate distributions, vol 1. Wiley, New York
33. Kelley JE (1960) The cutting-plane method for solving convex programs. J Soc Indus Appl Math 8(4):703-712
34. Komninakis C, Vandenberghe L, Wesel RD (2001) Capacity of the binomial channel or minimax redundancy for memoryless channels. In: Proceedings of IEEE international symposium on information theory, Washington, p 127 (Pubitemid 32795370)
35. Kostal L (2010) Information capacity in the weak-signal approximation. Phys Rev E 82(026):115
36. Kostal L (2012) Approximate information capacity of the perfect integrate-and-fire neuron using the temporal code. Brain Res 1434:136-141
37. Kostal L, Lansky P (2010) Information transfer with small-amplitude signals. Phys Rev E 81:050,901(R)
38. Kostal L, Lansky P (2013) Information transfer under metabolic constraints in a simple homogeneous population of olfactory neurons (manuscript submitted)
39. Kostal L, Lansky P, Rospars JP (2008) Efficient olfactory coding in the pheromone receptor neuron of a moth. PLoS Comp Biol 4:e1000,053
40. Lansky P, Sacerdote L (2001) The Ornstein-Uhlenbeck neuronal model with signal-dependent noise. Phys Lett A 285(3-4):132-140 (Pubitemid 32655768)
41. Lansky P, Pokora O, Rospars JP (2008) Classification of stimuli based on stimulus-response curves and their variability. Brain Res 1225:57-66
42. Laughlin SB (1981) A simple coding procedure enhances a neuron's information capacity. Z Naturforsch 36(9-10):910-912
43. Laughlin SB, de Ruyter van Steveninck RR, Anderson JC (1998) The metabolic cost of neural information. Nat Neurosci 1(1):36-41 (Pubitemid 128652639)
44. Levy WB, Baxter RA (1996) Energy efficient neural codes. Neural Comput 8(3):531-543 (Pubitemid 126450623)
45. Levy WB, Baxter RA (2002) Energy-efficient neuronal computation via quantal synaptic failures. J Neurosci 22(11):4746-4755 (Pubitemid 37465690)
46. Machens CK, Gollisch T, Kolesnikova O, Herz AVM (2005) Testing the efficiency of sensory coding with optimal stimulus ensembles. Neuron 47(3):447-456 (Pubitemid 41073884)
47. McDonnell MD, Flitney AP (2009) Signal acquisition via polarization modulation in single photon sources. Phys Rev E 80:060,102(R)
48. McDonnell MD, Stocks NG (2008) Maximally informative stimuli and tuning curves for sigmoidal rate-coding neurons and populations. Phys Rev Lett 101(5):058,103
49. McDonnell MD, Mohan A, Stricker C, Ward LM (2012) Input-rate modulation of gamma oscillations is sensitive to network topology, delays and short-term plasticity. Brain Res 1434:162-177
50. McEliece RJ (2002) The theory of information and coding. Cambridge University Press, Cambdridge
51. Moujahid A, d'Anjou A, Torrealdea FJ (2011) Energy and information in Hodgkin-Huxley neurons. Phys Rev E 83(031):912
52. Mountcastle VB, Poggio GF, Werner G (1963) The relation of thalamic cell response to peripheral stimuli varied over an intensive continuum. J Neurophysiol 26(5):807-834
53. Nadal JP, Bonnasse-Gahot L (2012) Perception of categories: from coding efficiency to reaction times. Brain Res 1434:47-61
54. Nikitin AP, Stocks NG, Morse RP, McDonnell MD (2009) Neural population coding is optimized by discrete tuning curves. Phys Rev Lett 103(138):101
55. Pawlas Z, Klebanov LB, Prokop M, Lansky P (2008) Parameters of spike trains observed in a short time window. Neural Comput 20(5):1325-1343
56. Quastler H (1953) Essays on the use of information theory in biology. University of Illinois Press, Champaign
57. Quiroga RQ, Panzeri S (2009) Extracting information from neuronal populations: information theory and decoding approaches. Nat Rev Neurosci 10:173-185
58. Rieke F, de Ruyter van Steveninck RR, Warland D, Bialek W (1997) Spikes: exploring the neural code. MIT Press, Cambridge
59. Rissanen JJ (1996) Fisher information and stochastic complexity. IEEE Trans Inf Theory 42(1):40-47 (Pubitemid 126775655)
60. Sadeghi P, Vontobel PO, Shams R (2009) Optimization of information rate upper and lower bounds for channels with memory. IEEE Trans Inf Theory 55(2):663-688
61. Schreiber S, Machens CK, Herz AVM, Laughlin SB (2002) Energy-efficient coding with discrete stochastic events. Neural Comput 14:1323-1346
62. Schroeder DJ (1999) Astronomical optics. Academic Press, San Diego
63. Shadlen MN, Newsome WT (1998) The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J Neurosci 18(10):3870-3896 (Pubitemid 28225693)
64. Smith JG (1971) The information capacity of amplitude-and variance-constrained sclar gaussian channels. Inform Control 18(3):203-219
65. Stein RB (1967) The information capacity of nerve cells using a frequency code. Biophys J 7(6):797-826
66. Stein RB, Gossen ER, Jones KE (2005) Neuronal variability: noise or part of the signal? Nat Rev Neurosci 6(5):389-397 (Pubitemid 40593484)
67. de Ruyter van Steveninck RR, Laughlin SB (1996) The rate of information transfer at graded-potential synapses. Nature 379(6566):642-644 (Pubitemid 26052825)
68. Suksompong P, Berger T (2010) Capacity analysis for integrate-and-fire neurons with descending action potential thresholds. IEEE Trans Inf Theory 56(2):838-851
69. Tchamkerten A (2004) On the discreteness of capacity-achieving distributions. IEEE Trans Inf Theory 50(11):2773-2778
70. Tuckwell HC (1988) Introduction to theoretical neurobiology, vol 2. Cambridge University Press, New York
71. Verdu S (1990) On channel capacity per unit cost. IEEE Trans Inf Theory 36(5):1019-1030 (Pubitemid 20738361)
72. Wainrib G, Thieullen M, Pakdaman K (2010) Intrinsic variability of latency to first-spike. Biol Cyb 103:43-56
73. Weinstock R (1974) Calculus of variations. Dover, New York
74. Wiener MC, Richmond BJ (1999) Using response models to estimate channel capacity for neuronal classification of stationary visual stimuli using temporal coding. J Neurophysiol 82(6):2861-2875 (Pubitemid 30017418)